Comparing to other recent methods available in literature, we found that the proposed algorithm accomplished better result in terms of sensibility (94.27%) and specificity (97.63%).ĭiabetes is among number of diseases that involve problems with the hormone insulin. This tool provides a unified framework for benchmarking the methods, but also points out clear deficiencies in the current practice in the method development. It consists of around 89 colour fundus images of which 84 contain at least mild non-proliferative signs of the diabetic retinopathy. DIARETDB1 gathers high-quality medical images which have been verified by experts. Evaluations were done with retinal fundus image DIARETDB1 database. Finally, according to size criteria, we eliminate the other regions obtain the regions of interest related to exudates. In the last step, we compute the result image with an algorithm based on Entropy Maximization Thresholding to obtain two segmented regions (optical disk and exudates) which were highlighted in the second step. The second step refers to blood vessel removal by using morphological operators. The difference between filtered image and his inverse reduces noise and removes background while preserving features and patterns related to the exudates. It focuses on histogram expansion and median filter. The first step consists of image enhancement. This paper presents an automated method for exudates detection in digital retinal fundus image.
Early detection through automatic screening programs reduces considerably expansion of the disease. Among them, diabetic retinopathy is nowadays a chronic disease that attacks most of diabetic patients. It happens with vision deficiencies that are pathologic states due to many ocular diseases. Blindness which is considered as degrading disabling disease is the final stage that occurs when a certain threshold of visual acuity is overlapped.
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